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Rand Waltzman
Program Manager, Information Innovation Office




     Anomaly Detection at Multiple Scales

                DARPA Cyber Colloquium
                    Arlington, VA

                      November 7, 2011




             Approved for Public Release, Distribution Unlimited.
The problem…

•   Why didn’t we see it coming?
     •   Robert Hanssen
     •   Aldrich Ames
     •   Ana Belen Montes


•   The trail of evidence was obvious after the fact

•   Why is it so hard to pick up the trail before the fact?

•   Answer:
     •   Difficult to characterize anomalous v normal behaviors
     •   Malicious activities distributed over time and cyberspace
     •   Weak signal in a noisy background
     •   Enormous amount of data




                                Approved for Public Release, Distribution Unlimited
How much data?

•   Find evidence of an insider at Fort Hood:
     •   65,000 soldiers at Fort Hood
     •   Represent the e-mail and text message traffic as a graph
          • Nodes represent persons
          • Links represent e-mail or text messages
     •   Analyze 47,201,879,000 links between 2,336,726 nodes over one year


•   Find evidence against one person over the entire DoD:
     •   E-mail and text message traffic only
     •   Analyze 755,230,064,000 links between 37,387,616 nodes over one year


•   And this does not include web-searches, file accesses, applications run, and
    many other forms of cyber observable behavioral data.




                                Approved for Public Release, Distribution Unlimited
Anomaly Detection at Multiple Scales (ADAMS)

•   Focus on malevolent insiders that started out as good guys
•   Research organized into four coordinated thrusts
     •   Topic analysis
          • Develop signatures for areas of responsibility
          • Detect straying from tasked topic areas, or produces unexpected content
     •   System use
          • Temporal sequences of system and file accesses
          • Patterns of behavior
     •   Social interactions and networks
          • Indicators
          • Social exchanges
     •   Psychological state
          • Personal temperament and mental health
          • Distress, instability, or other vulnerability


                          Detect the signs that they are turning
                            before or shortly after they turn


                                   Approved for Public Release, Distribution Unlimited
Example: Insider Threat Scenarios in
            StackOverflow.com

•   Data set with 645 thousand users, 5.5 million question posts, and 12 million
    responses
•   Use of human controlled alias accounts (aka Sock Puppets) for voting fraud
•   9 inserted sock puppet voting fraud schemes
•   Oregon State University graph analytics detected 7 out of 9 schemes and
    310 out of 535 sock puppets.




                           Approved for Public Release, Distribution Unlimited.
Contact Information

• If you would like to pursue topics discussed in this presentation,
  please send your ideas to
• rand.waltzman@darpa.mil




                         Approved for Public Release, Distribution Unlimited

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Anomaly Detection at Multiple Scales (Waltzman)

  • 1. Rand Waltzman Program Manager, Information Innovation Office Anomaly Detection at Multiple Scales DARPA Cyber Colloquium Arlington, VA November 7, 2011 Approved for Public Release, Distribution Unlimited.
  • 2. The problem… • Why didn’t we see it coming? • Robert Hanssen • Aldrich Ames • Ana Belen Montes • The trail of evidence was obvious after the fact • Why is it so hard to pick up the trail before the fact? • Answer: • Difficult to characterize anomalous v normal behaviors • Malicious activities distributed over time and cyberspace • Weak signal in a noisy background • Enormous amount of data Approved for Public Release, Distribution Unlimited
  • 3. How much data? • Find evidence of an insider at Fort Hood: • 65,000 soldiers at Fort Hood • Represent the e-mail and text message traffic as a graph • Nodes represent persons • Links represent e-mail or text messages • Analyze 47,201,879,000 links between 2,336,726 nodes over one year • Find evidence against one person over the entire DoD: • E-mail and text message traffic only • Analyze 755,230,064,000 links between 37,387,616 nodes over one year • And this does not include web-searches, file accesses, applications run, and many other forms of cyber observable behavioral data. Approved for Public Release, Distribution Unlimited
  • 4. Anomaly Detection at Multiple Scales (ADAMS) • Focus on malevolent insiders that started out as good guys • Research organized into four coordinated thrusts • Topic analysis • Develop signatures for areas of responsibility • Detect straying from tasked topic areas, or produces unexpected content • System use • Temporal sequences of system and file accesses • Patterns of behavior • Social interactions and networks • Indicators • Social exchanges • Psychological state • Personal temperament and mental health • Distress, instability, or other vulnerability Detect the signs that they are turning before or shortly after they turn Approved for Public Release, Distribution Unlimited
  • 5. Example: Insider Threat Scenarios in StackOverflow.com • Data set with 645 thousand users, 5.5 million question posts, and 12 million responses • Use of human controlled alias accounts (aka Sock Puppets) for voting fraud • 9 inserted sock puppet voting fraud schemes • Oregon State University graph analytics detected 7 out of 9 schemes and 310 out of 535 sock puppets. Approved for Public Release, Distribution Unlimited.
  • 6. Contact Information • If you would like to pursue topics discussed in this presentation, please send your ideas to • rand.waltzman@darpa.mil Approved for Public Release, Distribution Unlimited